Joint Model for Estimating the Asymmetric Distribution of Medical Costs Based on a History Process
Bibliographic record
Abstract
In this paper, we modify a semi-parameter estimation of the joint model for the mean medical cost function with time-dependent covariates to enable it to describe the nonlinear relationship between the longitudinal variable and time points by using polynomial approximation. The observation time points are discrete and not exactly the same for all subjects; in order to use all of the information, we first estimate the mean medical cost at the same observed time points for all subjects, and then we weigh these values using the kernel method. Therefore, a smooth mean function of medical costs can be obtained. The proposed estimating method can be used for asymmetric distribution statistics. The consistency of the estimator is demonstrated by theoretical analysis. For the simulation study, we first set up the values of parameters and non-parametric functions, and then we generated random samples for covariates and censored survival times. Finally, the longitudinal data of response variables could be produced based on the covariates and survival times. Then, numerical simulation experiments were conducted by using the proposed method and applying the JM package in R to the generated data. The estimated results for parameters and non-parametric functions were compared with different settings. Numerical results illustrate that the standard deviations of the parametric estimators decrease as the sample sizes increases and are much smaller than preassigned threshold value. The estimates of non-parametric functions in the model almost coincide with the true functions as shown in the figures of simulation study. We apply the proposed model to a real data set from a multicenter automatic defibrillator implantation trial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".